Régularisation dans les Modèles Linéaires Généralisés Mixtes avec effet aléatoire autorégressif

Jocelyn Chauvet; Catherine Trottier; Xavier Bry

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arXiv · 1908.07478

Régularisation dans les Modèles Linéaires Généralisés Mixtes avec effet aléatoire autorégressif

Jocelyn Chauvet·Catherine Trottier·Xavier Bry

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Abstract

We address regularised versions of the Expectation-Maximisation (EM) algorithm for Generalised Linear Mixed Models (GLMM) in the context of panel data (measured on several individuals at different time points). A random response y is modelled by a GLMM, using a set X of explanatory variables and two random effects. The first effect introduces the dependence within individuals on which data is repeatedly collected while the second embodies the serially correlated time-specific effect shared by all the individuals. Variables in X are assumed many and redundant, so that regression demands regularisation. In this context, we first propose a L2-penalised EM algorithm for low-dimensional data, and then a supervised component-based regularised EM algorithm for the high-dimensional case.

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Jocelyn Chauvet, Catherine Trottier, Xavier Bry. 2019-08-09. Régularisation dans les Modèles Linéaires Généralisés Mixtes avec effet aléatoire autorégressif. https://arxiv.org/abs/1908.07478

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